Automated synthesis of feature functions for pattern detection

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Pei-Fang Guo and Prabir Bhattacharya and 
                 Nawwaf Kharma",
  title =        "Automated synthesis of feature functions for pattern
  booktitle =    "23rd Canadian Conference on Electrical and Computer
                 Engineering (CCECE), 2010",
  year =         "2010",
  month =        "2-5 " # may,
  abstract =     "In pattern detection systems, the general techniques
                 of feature extraction and selection perform linear
                 transformations from primitive feature vectors to new
                 vectors of lower dimensionality. At times, new
                 extracted features might be linear combinations of some
                 primitive features that are not able to provide better
                 classification accuracy. To solve this problem, we
                 propose the integration of genetic programming and the
                 expectation maximisation algorithm (GP-EM) to
                 automatically synthesise feature functions based on
                 primitive input features for breast cancer detection.
                 With the Gaussian mixture model, the proposed algorithm
                 is able to perform nonlinear transformations of
                 primitive feature vectors and data modelling
                 simultaneously. Compared to the performance of other
                 algorithms, such us the support vector machine,
                 multi-layer perceptrons, inductive machine learning and
                 logistic regression, which all used the entire
                 primitive feature set, the proposed algorithm achieves
                 a higher recognition rate by using one single
                 synthesised feature function.",
  keywords =     "genetic algorithms, genetic programming, Gaussian
                 mixture model, automated synthesis, breast cancer
                 detection, data modelling, expectation maximization
                 algorithm, feature extraction, feature functions,
                 inductive machine learning, logistic regression,
                 multilayer perceptrons, pattern detection systems,
                 primitive feature vector nonlinear transformations,
                 support vector machine, cancer, data models,
                 expectation-maximisation algorithm, feature extraction,
                 medical computing, object detection, pattern
                 classification, vectors",
  DOI =          "doi:10.1109/CCECE.2010.5575224",
  ISSN =         "0840-7789",
  notes =        "Pei-Fang Guo PhD A Gaussian Mixture-Based Approach to
                 Synthesizing Nonlinear Feature Functions for Automated
                 Object Detection

                 Concordia University 2010

                 Also known as \cite{5575224}",

Genetic Programming entries for Pei Fang Guo Prabir Bhattacharya Nawwaf Kharma